paper "Network-based Prediction of Drug-Target Interactions using an Arbitrary-Order Proximity Embedded Deep Forest"
drug_dict.txt
: list of drug unique identifier and drug namesprotein_dict.txt
: list of protein unique identifier and protein namesdisease_dict.txt
: list of disease unique identifier and disease namesse_dict.txt
: list of side effect unique identifier and side effect namesdrugdrug.txt
: Drug-Drug interaction matrixdrugDisease.txt
: Drug-Disease association matrixdrugsideEffect.txt
: Drug-SideEffect association matrixdrugsim1network.txt
: Drug chemical similarity matrixdrugsim2network.txt
: Drug therapeutic similarity matrixdrugsim3network.txt
: Drug sequence similarity matrixdrugsim4network.txt
: Drug biological processes similarity matrixdrugsim5network.txt
: Drug cellular component similarity matrixdrugsim6network.txt
: Drug molecular function similarity matrixproteinprotein.txt
: Protein-Protein interaction matrixproteinDisease.txt
: Protein-Disease association matrixproteinsim1network.txt
: Protein sequence similarity matrixproteinsim2network.txt
: Protein biological processes similarity matrixproteinsim3network.txt
: Protein cellular component similarity matrixproteinsim4network.txt
: Protein molecular function similarity matrixSim_drugDisease
: Drug-Disease Jaccard similarity matrixSim_drugsideEffect.txt
: Drug-SideEffect Jaccard similarity matrixSim_proteinDisease.txt
: Protein-Disease Jaccard similarity matrix
Noted: Since drug-disease network, drug-side-effect network and protein-disease network are heterogeneous network, we calculate the corresponding similarity networks based on the Jaccard similarity coefficient for them
This directory contains code necessary to use AROPE to extract arbitrary-Order proximity from different network
This directory contains library of deep forest classifier
$ python AOPEDF.py